This invention relates generally to traffic control systems. More specifically, the present invention relates to a traffic control system that optimizes traffic flow based on information obtained via a wireless telephone network.
Problems in traffic control have been studied extensively over the last few decades. Conventional traffic control systems comprise three major components: hardware infrastructure, information gathering systems, and traffic control software including mathematical models and algorithms. At present we are primarily interested in software models and algorithms, and in information gathering systems.
Due to ever increasing traffic volume, traffic control and information acquisition have become an important part of the overall traffic management strategy.
Generally, dynamic traffic data are gathered by three methods:
1. Road sensor devices such as induction loops, traffic detectors, and TV cameras mounted on poles;
2. Mobile traffic units such as police, road service, helicopters, weather reporting devices, etc.
3. Mobile positioning and communication systems using GPS devices or similar vehicle-tracking equipment.
The disadvantages of these data collection methods are summarized as follows:
1. Relatively high cost of required capital investment into road devices especially when carried out within existing road infrastructures;
2. Relatively limited number of organizations such as trucking, delivery and other service companies utilizing reporting vehicles equipped with GPS devices;
3. In general only small geographical areas are effectively covered due to specific nature of service tasks, apart from the relatively small number of cars equipped with required GPS devices necessary for precise position determination.
In a recent development, GPS reporting devices have been mounted on individual cars to provide positioning information of vehicles via wireless mobile communication systems. The additional expenditures required by these mobile systems are much lower than by the traditional methods using fixed road metering. One disadvantage of these systems is in the relatively limited number of cars equipped with required GPS devices necessary for precise position determination, and therefore relatively small geographical areas that can be effectively covered.
As originally coined, the term xe2x80x9ctraffic controlxe2x80x9d implied a human operator, i.e. a policeman, or a specially trained dispatcher who directed traffic flows across road intersections. This xe2x80x9ccontrollerxe2x80x9d used his experience and intuition to evaluate traffic loads and waiting times in various directions and lanes, and for changing phase timing accordingly.
Following the introduction of electric traffic signals at the beginning of the twentieth century, progress in the methods of traffic control closely followed that of the control technology, and subsequently the progress of computer science.
Initially, simple electric clocks allocated a specific amount of time to each phase in a specific pattern to controlled traffic signals. These early clock systems were preset and provided no adjustment for peak traffic periods, or for unusual conditions.
The next step was to create a clock that operated differently at different times of the day, and used several different control patterns for different times of day. Those patterns were determined from historical data.
Starting in the mid-1960""s, computers were increasingly utilized in traffic control. These computers made it possible to create actuated controllers that had the ability to adjust the signal phase lengths in response to traffic flow in real time. If no vehicles were detected on an approach to an intersection, the controller could skip that phase or reduce the phase to a fixed minimum time. Thus, the green time for each approach was a function of the traffic flow, and could be varied between minimum and maximum lengths depending on traffic flows.
Modes of operation of modern traffic control systems can be divided into three primary categories: 1) pre-timed; 2) actuated (including both semi-actuated and fully actuated); and 3) traffic responsive. Under pre-timed operation, the master controller sets signal phases and cycle lengths on predetermined rates based on historical data.
An actuated controller operates based on traffic demands as registered by the actuation of vehicle and/or pedestrian detectors. There are several types of actuated controllers, but their main feature is the ability to adjust the phases in response to traffic flow.
A semi actuated controller maintains green on the major street except when vehicles are registered on minor streets, and then always return the right of way to the major street.
A fully actuated controller measures traffic flow on all approaches and makes assignments of the right of way in accordance with traffic demands. As such, a fully actuated controller requires placement of detectors on all approaches to the intersection. Thereby increasing installation and maintenance costs considerably.
In the traffic responsive mode, the system responds to inputs from traffic detectors and may react in one of the following ways:
Use vehicle volume data as measured by traffic detectors;
Perform pattern matchingxe2x80x94the volume and occupancy data from system detectors are compared with profiles in memory, and the most closely matching profile is used for decision making;
Perform future traffic predictionxe2x80x94projections of future conditions are computed based on data from traffic detectors.
A number of algorithms exist that purport to optimize performance of traffic responsive controllers that make use of various techniques such as linear programming, dynamic programming, fuzzy logic, regression analysis, and optimization and prediction procedures. The objective function that is usually set up to be optimized is some measure of overall traffic delay at an intersection or at a number of intersections, while the major control parameter for achieving this is the distribution of green and red light timings among different phases.
The usual framework for those algorithms is as follows. Signal timings should reflect the number of vehicles present on each approach to an intersection and the pattern of arrivals in the near future. The current queue lengths on each approach are identified by locating slow-moving and stationary traffic close to the stop-line. Algorithms minimize the total delay subject to certain constraints. Such constraints are:
1. Adequate capacity for all allowed traffic movements; and
2. Safety constraints (minimum number of seconds for green and inter-green times).
Minimization is performed over the pre-selected planning time horizon, which limits the forward time interval for which computations are made. As optimization is performed continuously, we have a rolling horizon framework.
The rate of delay on an approach is estimated as proportional to the number of vehicles in the queue. Accordingly, the total rate of delay at the intersection is the sum over all streams of these rates of delay. The objective function for optimization is the sum of those total rates of delay over the planning time period, which represents the total delay incurred. A slightly different formulation of the objective of optimization is minimization of the weighted sum of the estimated rate of delay and the number of stops per unit time for all traffic streams. In such a formulation the problem is amenable to treatment by mathematical optimization methods. In particular, by dynamic programming and linear programming techniques.
Most conventional attempts for real time responsive control are either optimized on a per intersection basis or make highly restrictive simplifying assumptions in treating multiple intersection problems. Still, there are a few works treating area-wide traffic control optimization problems. For example, U.S. Pat. No. 5,668,717 issued to James C. Spall proposed the use of neural networks that are able to learn patterns of traffic situations, store them for future use and modify them when the traffic situation changes.
It appears, though, that at the present time no widely accepted and approved method exists for optimizing traffic control signals on an area wide scale.
In view of the shortcomings of the prior art, it is an object of the present invention to provide a system and method for optimizing traffic flow based on information received from wireless telephone systems.
The above-identified disadvantages of the prior art systems may be overcome by using wireless networks as the sole means to provide location information. Technologically, this may be achieved by measuring the distances the signals traveling between a moving wireless (cellular) phone and a fixed set of base stations, and the times these signals take to travel. This information may then be applied to mathematical and statistical methods to solve the resulting equations.
This exemplary approach takes advantage of improved accuracy of measurement methods and of the large pool of wireless handsets that exist. For example, in the United States alone there are presently about 50 million such handsets. Furthermore, any necessary modifications, such as specialized location equipment, can be made on the network rather than on the handsets.
The present invention utilizes a cell phone network in which the data from moving vehicles are collected continuously and input into the system. The exemplary system filters and cleans the data by applying intelligent heuristic algorithms and produces accurate real time information on traffic situations that, in turn, can be supplied to automated traffic controllers. This eliminates the need for developing a dedicated mobile wireless information gathering fleet and other high cost devices requiring large capital investments and considerable work force.
Network system wide control is the means for real time adjustment of the timings of all signals in a traffic network to achieve a reduction in overall congestion consistent with the chosen system wide measure of effectiveness. This real time control is preferably responsive to instantaneous changes in traffic conditions including changes due to various traffic incidents. Also, the system is preferably adaptive in order to reflect daily and hourly non-recurring events, such as unexpected traffic pattern changes, temporary lane closures, etc., as well as long-term evolution in transportation systems like seasonal effects, permanent road changes, infrastructure development, etc. To achieve system wide optimization, the timings at different signalized intersections will not, in general, have predetermined relationships to one another except possibly for those signals along transportation arteries, where it will be preferable to synchronize the intersections.
The present invention utilizes an intelligent data gathering and processing system based on information flow from existing cellular phone networks, and uses such obtained cell phone based position data for real time computation of adjusted phase timings at signalized intersections.
The system of the present invention is capable of constructing and maintaining lists of vehicles moving along road sections at particular periods of time. This is achieved by tracking a predetermined number of in-vehicle cell phones within a given region. The exemplary system maintains a series of such lists associated with the previous elapsed time period and calculates estimates of the numbers of vehicles traveling on each particular road section, their actual traveling times, and the turning times and go-through times for all signalized intersections. Thus, the exemplary system is able to (1) compute real time traffic loads for various roads and road sections, (2) generate detailed lists and descriptions of vehicle turning movements, (3) compute real time turning data for all relevant intersections, and (4) estimate other relevant traffic parameters. The resulting information setup (with numerous relevant parameters estimated on the basis of observations) is then transferred with minimum delay to the automated traffic control system for the purpose of adjusting phase timings at signalized intersections for the next control time period. In other words, based on the traffic flow data obtained for the previous control time period, the system attempts to adjust phase timing at signalized intersections in such a way as to provide more green time for more heavy traffic flows at the expense of less loaded roadways for the next control time period. Roughly speaking, the longer travel time has been registered at a particular turn during the previous control time period, the more green light the intersection is going to get at the next time period.
This result may be achieved by maximizing a linear function in green light timings the coefficients of which are functions of time delays affected at all road sections during the previous control time period within the given region. Optimization is achieved under certain constraints, such as minimal and maximal values of green light timings, safety constraints expressing minimum number of seconds for inter-green times at each intersection, and other relevant constraints which could be set up individually for any turn and go-through of any signalized intersection, etc. The new values of green light timings obtained from the optimization will be applied to the next control time period during which new measurements of traffic travel times and traffic flows will be made as before, and the whole process will be repeated.